python - pandas standalone series and from dataframe different behavior -


here code , warning message. if change s standalone series using s = pd.series(np.random.randn(5)), there no such errors. using python 2.7 on windows.

it seems series created standalone , series created column of data frame different behavior? thanks.

my purpose change series value itself, other change on copy.

source code,

import pandas pd  sample = pd.read_csv('123.csv', header=none, skiprows=1,        dtype={0:str, 1:str, 2:str, 3:float}) sample.columns = pd.index(data=['c_a', 'c_b', 'c_c', 'c_d']) sample['c_d'] = sample['c_d'].astype('int64') s = sample['c_d'] #s = pd.series(np.random.randn(5)) in range(len(s)):     if s.iloc[i] > 0:         s.iloc[i] = s.iloc[i] + 1     else:         s.iloc[i] = s.iloc[i] - 1

warning message,

c:\python27\lib\site-packages\pandas\core\indexing.py:132: settingwithcopywarning:  value trying set on copy of slice dataframe  see caveats in documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy   self._setitem_with_indexer(indexer, value)

content of 123.csv,

c_a,c_b,c_c,c_d hello,python,numpy,0.0 hi,python,pandas,1.0 ho,c++,vector,0.0 ho,c++,std,1.0 go,c++,std,0.0

edit 1, seems lambda solution not work, tried print s before , after, same value,

import pandas pd  sample = pd.read_csv('123.csv', header=none, skiprows=1,        dtype={0:str, 1:str, 2:str, 3:float}) sample.columns = pd.index(data=['c_a', 'c_b', 'c_c', 'c_d']) sample['c_d'] = sample['c_d'].astype('int64') s = sample['c_d'] print s s.apply(lambda x:x+1 if x>0 else x-1) print s  0    0 1    1 2    0 3    1 4    0 name: c_d, dtype: int64 backend tkagg interactive backend. turning interactive mode on. 0    0 1    1 2    0 3    1 4    0

regards, lin

by doing s = sample['c_d'], if make change value of s original dataframe sample changes. that's why got warning.

you can s = sample[c_d].copy() instead, changing value of s doesn't change value of c_d column of dataframe sample.


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